[Udemy] Natural Language Processing With Transformers in Python (06.2021)

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[Udemy] Natural Language Processing With Transformers in Python (06.2021)

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种子哈希：15a0a25219359a8870cb3f3844c0e975a3826772
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收录时间：2022-01-29
最近下载：2025-02-16

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文件列表

  • 7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.mp4 121.8 MB
  • 8. Named Entity Recognition (NER)/9. NER With Sentiment.mp4 104.7 MB
  • 8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.mp4 93.3 MB
  • 7. Long Text Classification With BERT/2. Window Method in PyTorch.mp4 89.1 MB
  • 14. Fine-Tuning Transformer Models/5. The Logic of MLM.mp4 83.3 MB
  • 14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.mp4 81.8 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.mp4 80.8 MB
  • 14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.mp4 80.4 MB
  • 11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.mp4 78.9 MB
  • 14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.mp4 73.1 MB
  • 11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.mp4 71.4 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.mp4 65.5 MB
  • 8. Named Entity Recognition (NER)/10. NER With roBERTa.mp4 61.9 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.mp4 59.5 MB
  • 12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.mp4 58.5 MB
  • 2. NLP and Transformers/9. Positional Encoding.mp4 58.2 MB
  • 5. Language Classification/4. Tokenization And Special Tokens For BERT.mp4 58.1 MB
  • 8. Named Entity Recognition (NER)/1. Introduction to spaCy.mp4 54.2 MB
  • 4. Attention/2. Alignment With Dot-Product.mp4 51.5 MB
  • 14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).mp4 49.0 MB
  • 9. Question and Answering/7. Our First Q&A Model.mp4 47.9 MB
  • 14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.mp4 45.7 MB
  • 11. Reader-Retriever QA With Haystack/9. What is FAISS.mp4 45.0 MB
  • 12. [Project] Open-Domain QA/2. Creating the Database.mp4 44.5 MB
  • 14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).mp4 44.1 MB
  • 2. NLP and Transformers/10. Transformer Heads.mp4 41.8 MB
  • 11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.mp4 40.9 MB
  • 9. Question and Answering/4. Processing SQuAD Training Data.mp4 40.3 MB
  • 5. Language Classification/1. Introduction to Sentiment Analysis.mp4 39.3 MB
  • 1. Introduction/3. Environment Setup.mp4 39.1 MB
  • 8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.mp4 37.4 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).mp4 36.7 MB
  • 1. Introduction/2. Course Overview.mp4 36.0 MB
  • 10. Metrics For Language/3. Applying ROUGE to Q&A.mp4 35.6 MB
  • 13. Similarity/4. Using Cosine Similarity.mp4 35.5 MB
  • 4. Attention/6. Multi-head and Scaled Dot-Product Attention.mp4 35.5 MB
  • 8. Named Entity Recognition (NER)/2. Extracting Entities.mp4 35.2 MB
  • 2. NLP and Transformers/2. Pros and Cons of Neural AI.mp4 34.4 MB
  • 13. Similarity/3. Sentence Vectors With Mean Pooling.mp4 33.6 MB
  • 5. Language Classification/2. Prebuilt Flair Models.mp4 32.2 MB
  • 3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.mp4 31.9 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.mp4 31.6 MB
  • 9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.mp4 31.6 MB
  • 13. Similarity/2. Extracting The Last Hidden State Tensor.mp4 31.2 MB
  • 11. Reader-Retriever QA With Haystack/11. What is DPR.mp4 31.1 MB
  • 14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.mp4 30.7 MB
  • 4. Attention/3. Dot-Product Attention.mp4 30.4 MB
  • 9. Question and Answering/2. Retrievers, Readers, and Generators.mp4 30.1 MB
  • 14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.mp4 30.0 MB
  • 4. Attention/4. Self Attention.mp4 29.8 MB
  • 13. Similarity/1. Introduction to Similarity.mp4 29.6 MB
  • 8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.mp4 29.5 MB
  • 5. Language Classification/3. Introduction to Sentiment Models With Transformers.mp4 28.2 MB
  • 11. Reader-Retriever QA With Haystack/7. Cleaning the Index.mp4 27.7 MB
  • 14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.mp4 27.5 MB
  • 5. Language Classification/5. Making Predictions.mp4 27.2 MB
  • 9. Question and Answering/3. Intro to SQuAD 2.0.mp4 26.6 MB
  • 2. NLP and Transformers/6. Encoder-Decoder Attention.mp4 26.4 MB
  • 3. Preprocessing for NLP/2. Tokens Introduction.mp4 25.2 MB
  • 1. Introduction/4. CUDA Setup.mp4 24.9 MB
  • 11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.mp4 24.7 MB
  • 3. Preprocessing for NLP/1. Stopwords.mp4 24.2 MB
  • 13. Similarity/5. Similarity With Sentence-Transformers.mp4 24.1 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.mp4 23.7 MB
  • 2. NLP and Transformers/1. The Three Eras of AI.mp4 23.3 MB
  • 2. NLP and Transformers/3. Word Vectors.mp4 22.8 MB
  • 10. Metrics For Language/2. ROUGE in Python.mp4 22.7 MB
  • 10. Metrics For Language/4. Recall, Precision and F1.mp4 22.0 MB
  • 11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).mp4 21.9 MB
  • 14. Fine-Tuning Transformer Models/9. The Logic of NSP.mp4 21.9 MB
  • 2. NLP and Transformers/7. Self-Attention.mp4 21.8 MB
  • 11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.mp4 21.4 MB
  • 3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.mp4 21.2 MB
  • 11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).mp4 21.2 MB
  • 8. Named Entity Recognition (NER)/8. Entity Blacklist.mp4 21.1 MB
  • 3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.mp4 21.0 MB
  • 14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.mp4 20.8 MB
  • 3. Preprocessing for NLP/3. Model-Specific Special Tokens.mp4 19.8 MB
  • 10. Metrics For Language/6. Q&A Performance With ROUGE.mp4 19.7 MB
  • 8. Named Entity Recognition (NER)/7. Getting Entity Frequency.mp4 19.3 MB
  • 10. Metrics For Language/1. Q&A Performance With Exact Match (EM).mp4 19.0 MB
  • 3. Preprocessing for NLP/4. Stemming.mp4 18.1 MB
  • 2. NLP and Transformers/4. Recurrent Neural Networks.mp4 17.9 MB
  • 3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.mp4 17.8 MB
  • 9. Question and Answering/1. Open Domain and Reading Comprehension.mp4 16.9 MB
  • 4. Attention/1. Attention Introduction.mp4 16.6 MB
  • 10. Metrics For Language/5. Longest Common Subsequence (LCS).mp4 15.7 MB
  • 11. Reader-Retriever QA With Haystack/12. The DPR Architecture.mp4 15.0 MB
  • 14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.mp4 15.0 MB
  • 11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.mp4 14.6 MB
  • 2. NLP and Transformers/8. Multi-head Attention.mp4 14.0 MB
  • 11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.mp4 13.2 MB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.mp4 13.1 MB
  • 4. Attention/5. Bidirectional Attention.mp4 11.3 MB
  • 3. Preprocessing for NLP/5. Lemmatization.mp4 11.1 MB
  • 1. Introduction/1. Introduction.mp4 9.6 MB
  • 2. NLP and Transformers/5. Long Short-Term Memory.mp4 6.7 MB
  • 12. [Project] Open-Domain QA/1. ODQA Stack Structure.mp4 6.5 MB
  • 7. Long Text Classification With BERT/1. Classification of Long Text Using Windows.srt 24.8 kB
  • 8. Named Entity Recognition (NER)/9. NER With Sentiment.srt 20.1 kB
  • 7. Long Text Classification With BERT/2. Window Method in PyTorch.srt 16.7 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/3. Preprocessing.srt 15.5 kB
  • 14. Fine-Tuning Transformer Models/10. Fine-tuning with NSP - Data Preparation.srt 15.0 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/6. Build and Save.srt 14.4 kB
  • 4. Attention/2. Alignment With Dot-Product.srt 14.1 kB
  • 14. Fine-Tuning Transformer Models/7. Fine-tuning with MLM - Training.srt 14.0 kB
  • 14. Fine-Tuning Transformer Models/6. Fine-tuning with MLM - Data Preparation.srt 13.8 kB
  • 11. Reader-Retriever QA With Haystack/10. FAISS in Haystack.srt 13.7 kB
  • 14. Fine-Tuning Transformer Models/5. The Logic of MLM.srt 13.6 kB
  • 8. Named Entity Recognition (NER)/5. Pulling Data With The Reddit API.srt 13.2 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/7. Loading and Prediction.srt 12.0 kB
  • 11. Reader-Retriever QA With Haystack/13. Retriever-Reader Stack.srt 11.4 kB
  • 2. NLP and Transformers/10. Transformer Heads.srt 10.9 kB
  • 8. Named Entity Recognition (NER)/10. NER With roBERTa.srt 10.6 kB
  • 5. Language Classification/1. Introduction to Sentiment Analysis.srt 10.3 kB
  • 11. Reader-Retriever QA With Haystack/9. What is FAISS.srt 10.1 kB
  • 14. Fine-Tuning Transformer Models/1. Visual Guide to BERT Pretraining.srt 9.9 kB
  • 2. NLP and Transformers/9. Positional Encoding.srt 9.9 kB
  • 8. Named Entity Recognition (NER)/1. Introduction to spaCy.srt 9.6 kB
  • 5. Language Classification/2. Prebuilt Flair Models.srt 9.6 kB
  • 14. Fine-Tuning Transformer Models/3. BERT Pretraining - Masked-Language Modeling (MLM).srt 9.6 kB
  • 9. Question and Answering/7. Our First Q&A Model.srt 9.2 kB
  • 12. [Project] Open-Domain QA/3. Building the Haystack Pipeline.srt 9.2 kB
  • 14. Fine-Tuning Transformer Models/14. Fine-tuning with MLM and NSP - Data Preparation.srt 9.2 kB
  • 11. Reader-Retriever QA With Haystack/5. Elasticsearch in Haystack.srt 8.9 kB
  • 3. Preprocessing for NLP/9. Unicode Normalization - NFKD and NFKC.srt 8.9 kB
  • 10. Metrics For Language/3. Applying ROUGE to Q&A.srt 8.8 kB
  • 11. Reader-Retriever QA With Haystack/11. What is DPR.srt 8.7 kB
  • 3. Preprocessing for NLP/2. Tokens Introduction.srt 8.6 kB
  • 5. Language Classification/4. Tokenization And Special Tokens For BERT.srt 8.6 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/2. Getting the Data (Kaggle API).srt 8.6 kB
  • 1. Introduction/2. Course Overview.srt 8.3 kB
  • 13. Similarity/3. Sentence Vectors With Mean Pooling.srt 8.2 kB
  • 13. Similarity/1. Introduction to Similarity.srt 8.0 kB
  • 8. Named Entity Recognition (NER)/4. Authenticating With The Reddit API.srt 8.0 kB
  • 12. [Project] Open-Domain QA/2. Creating the Database.srt 7.9 kB
  • 2. NLP and Transformers/1. The Three Eras of AI.srt 7.9 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/5. Dataset Shuffle, Batch, Split, and Save.srt 7.8 kB
  • 1. Introduction/3. Environment Setup.srt 7.5 kB
  • 11. Reader-Retriever QA With Haystack/2. What is Elasticsearch.srt 7.4 kB
  • 4. Attention/6. Multi-head and Scaled Dot-Product Attention.srt 7.3 kB
  • 3. Preprocessing for NLP/3. Model-Specific Special Tokens.srt 7.3 kB
  • 5. Language Classification/3. Introduction to Sentiment Models With Transformers.srt 7.3 kB
  • 9. Question and Answering/2. Retrievers, Readers, and Generators.srt 7.2 kB
  • 9. Question and Answering/4. Processing SQuAD Training Data.srt 7.2 kB
  • 14. Fine-Tuning Transformer Models/4. BERT Pretraining - Next Sentence Prediction (NSP).srt 7.1 kB
  • 5. Language Classification/5. Making Predictions.srt 7.0 kB
  • 8. Named Entity Recognition (NER)/2. Extracting Entities.srt 6.9 kB
  • 8. Named Entity Recognition (NER)/6. Extracting ORGs From Reddit Data.srt 6.9 kB
  • 9. Question and Answering/3. Intro to SQuAD 2.0.srt 6.8 kB
  • 3. Preprocessing for NLP/6. Unicode Normalization - Canonical and Compatibility Equivalence.srt 6.7 kB
  • 3. Preprocessing for NLP/4. Stemming.srt 6.6 kB
  • 3. Preprocessing for NLP/1. Stopwords.srt 6.4 kB
  • 3. Preprocessing for NLP/8. Unicode Normalization - NFD and NFC.srt 6.4 kB
  • 4. Attention/4. Self Attention.srt 6.4 kB
  • 2. NLP and Transformers/6. Encoder-Decoder Attention.srt 6.2 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/4. Building a Dataset.srt 6.2 kB
  • 13. Similarity/4. Using Cosine Similarity.srt 6.0 kB
  • 3. Preprocessing for NLP/7. Unicode Normalization - Composition and Decomposition.srt 5.8 kB
  • 13. Similarity/2. Extracting The Last Hidden State Tensor.srt 5.8 kB
  • 10. Metrics For Language/1. Q&A Performance With Exact Match (EM).srt 5.7 kB
  • 4. Attention/3. Dot-Product Attention.srt 5.6 kB
  • 14. Fine-Tuning Transformer Models/13. The Logic of MLM and NSP.srt 5.6 kB
  • 10. Metrics For Language/4. Recall, Precision and F1.srt 5.6 kB
  • 2. NLP and Transformers/2. Pros and Cons of Neural AI.srt 5.6 kB
  • 11. Reader-Retriever QA With Haystack/7. Cleaning the Index.srt 5.4 kB
  • 14. Fine-Tuning Transformer Models/2. Introduction to BERT For Pretraining Code.srt 5.3 kB
  • 2. NLP and Transformers/3. Word Vectors.srt 5.2 kB
  • 9. Question and Answering/5. (Optional) Processing SQuAD Training Data with Match-Case.srt 5.2 kB
  • 2. NLP and Transformers/7. Self-Attention.srt 4.7 kB
  • 14. Fine-Tuning Transformer Models/9. The Logic of NSP.srt 4.7 kB
  • 10. Metrics For Language/2. ROUGE in Python.srt 4.6 kB
  • 2. NLP and Transformers/4. Recurrent Neural Networks.srt 4.6 kB
  • 3. Preprocessing for NLP/5. Lemmatization.srt 4.3 kB
  • 11. Reader-Retriever QA With Haystack/6. Sparse Retrievers.srt 4.3 kB
  • 10. Metrics For Language/6. Q&A Performance With ROUGE.srt 4.2 kB
  • 13. Similarity/5. Similarity With Sentence-Transformers.srt 4.2 kB
  • 8. Named Entity Recognition (NER)/8. Entity Blacklist.srt 4.1 kB
  • 8. Named Entity Recognition (NER)/7. Getting Entity Frequency.srt 4.0 kB
  • 11. Reader-Retriever QA With Haystack/1. Intro to Retriever-Reader and Haystack.srt 3.9 kB
  • 9. Question and Answering/1. Open Domain and Reading Comprehension.srt 3.6 kB
  • 1. Introduction/4. CUDA Setup.srt 3.6 kB
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/1. Project Overview.srt 3.5 kB
  • 14. Fine-Tuning Transformer Models/8. Fine-tuning with MLM - Training with Trainer.srt 3.4 kB
  • 14. Fine-Tuning Transformer Models/11. Fine-tuning with NSP - DataLoader.srt 3.4 kB
  • 2. NLP and Transformers/8. Multi-head Attention.srt 3.3 kB
  • 1. Introduction/1. Introduction.srt 3.2 kB
  • 10. Metrics For Language/5. Longest Common Subsequence (LCS).srt 3.1 kB
  • 4. Attention/5. Bidirectional Attention.srt 3.0 kB
  • 4. Attention/1. Attention Introduction.srt 2.8 kB
  • 11. Reader-Retriever QA With Haystack/8. Implementing a BM25 Retriever.srt 2.6 kB
  • 11. Reader-Retriever QA With Haystack/12. The DPR Architecture.srt 2.3 kB
  • 2. NLP and Transformers/5. Long Short-Term Memory.srt 2.2 kB
  • 11. Reader-Retriever QA With Haystack/3. Elasticsearch Setup (Windows).srt 2.1 kB
  • 11. Reader-Retriever QA With Haystack/4. Elasticsearch Setup (Linux).srt 2.1 kB
  • 12. [Project] Open-Domain QA/1. ODQA Stack Structure.srt 2.0 kB
  • 11. Reader-Retriever QA With Haystack/2.1 Elasticsearch (Cloud) Introduction Article.html 195 Bytes
  • 11. Reader-Retriever QA With Haystack/11.1 Article.html 189 Bytes
  • 11. Reader-Retriever QA With Haystack/12.1 Article.html 189 Bytes
  • 7. Long Text Classification With BERT/1.1 Article.html 188 Bytes
  • 9. Question and Answering/5.2 Pattern Matching Article.html 184 Bytes
  • 5. Language Classification/3.1 Notebook.html 181 Bytes
  • 5. Language Classification/4.1 Notebook.html 181 Bytes
  • 5. Language Classification/5.1 Notebook.html 181 Bytes
  • 12. [Project] Open-Domain QA/3.1 Notebook.html 180 Bytes
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/7.1 Notebook.html 179 Bytes
  • 5. Language Classification/1.1 Notebook.html 178 Bytes
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/6.1 Notebook.html 178 Bytes
  • 7. Long Text Classification With BERT/2.1 Notebook.html 178 Bytes
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/4.1 Notebook.html 177 Bytes
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/5.1 Notebook.html 177 Bytes
  • 8. Named Entity Recognition (NER)/10.1 Notebook.html 177 Bytes
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/2.1 Notebook.html 176 Bytes
  • 6. [Project] Sentiment Model With TensorFlow and Transformers/3.1 Notebook.html 176 Bytes
  • 8. Named Entity Recognition (NER)/6.2 Notebook.html 176 Bytes
  • 8. Named Entity Recognition (NER)/7.1 Notebook.html 176 Bytes
  • 8. Named Entity Recognition (NER)/8.1 Notebook.html 176 Bytes
  • 9. Question and Answering/4.1 Notebook.html 175 Bytes
  • 12. [Project] Open-Domain QA/2.2 Notebook.html 174 Bytes
  • 5. Language Classification/2.1 Notebook.html 174 Bytes
  • 8. Named Entity Recognition (NER)/4.1 Notebook.html 174 Bytes
  • 8. Named Entity Recognition (NER)/5.1 Notebook.html 174 Bytes
  • 7. Long Text Classification With BERT/1.2 Notebook.html 173 Bytes
  • 8. Named Entity Recognition (NER)/9.1 Notebook.html 172 Bytes
  • 8. Named Entity Recognition (NER)/6.1 Data.html 171 Bytes
  • 11. Reader-Retriever QA With Haystack/9.1 Article.html 170 Bytes
  • 8. Named Entity Recognition (NER)/1.1 Notebook.html 169 Bytes
  • 8. Named Entity Recognition (NER)/2.1 Notebook.html 169 Bytes
  • 11. Reader-Retriever QA With Haystack/5.1 Notebook.html 168 Bytes
  • 11. Reader-Retriever QA With Haystack/6.1 Notebook.html 168 Bytes
  • 11. Reader-Retriever QA With Haystack/7.1 Notebook.html 168 Bytes
  • 11. Reader-Retriever QA With Haystack/8.1 Notebook.html 168 Bytes
  • 9. Question and Answering/5.1 Notebook.html 167 Bytes
  • 11. Reader-Retriever QA With Haystack/10.1 Notebook.html 166 Bytes
  • 2. NLP and Transformers/2.3 Self-Driving Limitations.html 163 Bytes
  • 4. Attention/5.1 Notebook.html 163 Bytes
  • 9. Question and Answering/7.1 Notebook.html 163 Bytes
  • 10. Metrics For Language/3.1 Notebook.html 162 Bytes
  • 11. Reader-Retriever QA With Haystack/9.2 Notebook.html 162 Bytes
  • 9. Question and Answering/3.1 Notebook.html 162 Bytes
  • 4. Attention/2.1 Notebook.html 161 Bytes
  • 4. Attention/3.1 Notebook.html 161 Bytes
  • 10. Metrics For Language/1.1 Notebook.html 160 Bytes
  • 11. Reader-Retriever QA With Haystack/11.2 Notebook.html 160 Bytes
  • 11. Reader-Retriever QA With Haystack/12.2 Notebook.html 160 Bytes
  • 9. Question and Answering/1.1 Notebook.html 160 Bytes
  • 9. Question and Answering/2.1 Notebook.html 160 Bytes
  • 14. Fine-Tuning Transformer Models/14.1 Notebook.html 159 Bytes
  • 14. Fine-Tuning Transformer Models/8.1 Notebook.html 159 Bytes
  • 2. NLP and Transformers/2.1 2010 Flash Crash.html 159 Bytes
  • 4. Attention/6.1 Notebook.html 159 Bytes
  • 11. Reader-Retriever QA With Haystack/1.1 Notebook.html 157 Bytes
  • 3. Preprocessing for NLP/5.1 Notebook.html 157 Bytes
  • 3. Preprocessing for NLP/6.1 Notebook.html 157 Bytes
  • 3. Preprocessing for NLP/7.1 Notebook.html 157 Bytes
  • 3. Preprocessing for NLP/8.1 Notebook.html 157 Bytes
  • 3. Preprocessing for NLP/9.1 Notebook.html 157 Bytes
  • 14. Fine-Tuning Transformer Models/13.1 Notebook.html 156 Bytes
  • 11. Reader-Retriever QA With Haystack/13.1 Notebook.html 155 Bytes
  • 10. Metrics For Language/2.1 Notebook.html 154 Bytes
  • 10. Metrics For Language/4.1 Notebook.html 154 Bytes
  • 10. Metrics For Language/5.1 Notebook.html 154 Bytes
  • 10. Metrics For Language/6.1 Notebook.html 154 Bytes
  • 14. Fine-Tuning Transformer Models/5.1 Notebook.html 154 Bytes
  • 14. Fine-Tuning Transformer Models/9.1 Notebook.html 154 Bytes
  • 4. Attention/4.1 Notebook.html 154 Bytes
  • 3. Preprocessing for NLP/1.1 Notebook.html 153 Bytes
  • 3. Preprocessing for NLP/4.1 Notebook.html 152 Bytes
  • 14. Fine-Tuning Transformer Models/10.1 Notebook.html 151 Bytes
  • 14. Fine-Tuning Transformer Models/11.1 Notebook.html 151 Bytes
  • 14. Fine-Tuning Transformer Models/6.1 Notebook.html 151 Bytes
  • 14. Fine-Tuning Transformer Models/7.1 Notebook.html 151 Bytes
  • 3. Preprocessing for NLP/2.1 Notebook.html 150 Bytes
  • 3. Preprocessing for NLP/3.1 Notebook.html 150 Bytes
  • 4. Attention/1.1 Notebook.html 147 Bytes
  • 12. [Project] Open-Domain QA/2.1 Data.html 145 Bytes
  • 2. NLP and Transformers/2.2 Amazon AI Recruitment Bias.html 144 Bytes
  • 14. Fine-Tuning Transformer Models/2.1 Notebook.html 143 Bytes
  • 14. Fine-Tuning Transformer Models/3.1 Notebook.html 143 Bytes
  • 14. Fine-Tuning Transformer Models/4.1 Notebook.html 143 Bytes
  • 14. Fine-Tuning Transformer Models/12. Setup the NSP Fine-tuning Training Loop.html 136 Bytes
  • 14. Fine-Tuning Transformer Models/15. Setup DataLoader and Model Fine-tuning For MLM and NSP.html 136 Bytes
  • 8. Named Entity Recognition (NER)/3. NER Walkthrough.html 136 Bytes
  • 9. Question and Answering/6. Processing SQuAD Dev Data.html 136 Bytes
  • 1. Introduction/3.1 Installation Instructions.html 129 Bytes
  • 1. Introduction/4.1 Installation Instructions.html 129 Bytes
  • 1. Introduction/2.1 GitHub Repo.html 103 Bytes
  • 8. Named Entity Recognition (NER)/1.2 spaCy Model Docs.html 84 Bytes

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